Trust for Intelligent Recommendation

Nonfiction, Computers, Advanced Computing, Information Technology, Database Management, General Computing
Cover of the book Trust for Intelligent Recommendation by Touhid Bhuiyan, Springer New York
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Touhid Bhuiyan ISBN: 9781461468950
Publisher: Springer New York Publication: March 30, 2013
Imprint: Springer Language: English
Author: Touhid Bhuiyan
ISBN: 9781461468950
Publisher: Springer New York
Publication: March 30, 2013
Imprint: Springer
Language: English

Recommender systems are one of the recent inventions to deal with the ever-growing information overload in relation to the selection of goods and services in a global economy. Collaborative Filtering (CF) is one of the most popular techniques in recommender systems. The CF recommends items to a target user based on the preferences of a set of similar users known as the neighbors, generated from a database made up of the preferences of past users. In the absence of these ratings, trust between the users could be used to choose the neighbor for recommendation making. Better recommendations can be achieved using an inferred trust network which mimics the real world “friend of a friend” recommendations. To extend the boundaries of the neighbor, an effective trust inference technique is required.

This book proposes a trust interference technique called Directed Series Parallel Graph (DSPG) that has empirically outperformed other popular trust inference algorithms, such as TidalTrust and MoleTrust. For times when reliable explicit trust data is not available, this book outlines a new method called SimTrust for developing trust networks based on a user’s interest similarity. To identify the interest similarity, a user’s personalized tagging information is used. However, particular emphasis is given in what resources the user chooses to tag, rather than the text of the tag applied. The commonalities of the resources being tagged by the users can be used to form the neighbors used in the automated recommender system. Through a series of case studies and empirical results, this book highlights the effectiveness of this tag-similarity based method over the traditional collaborative filtering approach, which typically uses rating data.

Trust for Intelligent Recommendation is intended for practitioners as a reference guide for developing improved, trust-based recommender systems. Researchers in a related field will also find this book valuable.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

Recommender systems are one of the recent inventions to deal with the ever-growing information overload in relation to the selection of goods and services in a global economy. Collaborative Filtering (CF) is one of the most popular techniques in recommender systems. The CF recommends items to a target user based on the preferences of a set of similar users known as the neighbors, generated from a database made up of the preferences of past users. In the absence of these ratings, trust between the users could be used to choose the neighbor for recommendation making. Better recommendations can be achieved using an inferred trust network which mimics the real world “friend of a friend” recommendations. To extend the boundaries of the neighbor, an effective trust inference technique is required.

This book proposes a trust interference technique called Directed Series Parallel Graph (DSPG) that has empirically outperformed other popular trust inference algorithms, such as TidalTrust and MoleTrust. For times when reliable explicit trust data is not available, this book outlines a new method called SimTrust for developing trust networks based on a user’s interest similarity. To identify the interest similarity, a user’s personalized tagging information is used. However, particular emphasis is given in what resources the user chooses to tag, rather than the text of the tag applied. The commonalities of the resources being tagged by the users can be used to form the neighbors used in the automated recommender system. Through a series of case studies and empirical results, this book highlights the effectiveness of this tag-similarity based method over the traditional collaborative filtering approach, which typically uses rating data.

Trust for Intelligent Recommendation is intended for practitioners as a reference guide for developing improved, trust-based recommender systems. Researchers in a related field will also find this book valuable.

More books from Springer New York

Cover of the book Sleep Deprivation and Disease by Touhid Bhuiyan
Cover of the book An Historical Analysis of Skin Color Discrimination in America by Touhid Bhuiyan
Cover of the book The Symbolism of Globalization, Development, and Aging by Touhid Bhuiyan
Cover of the book Sustainable Web Ecosystem Design by Touhid Bhuiyan
Cover of the book Micro-Relay Technology for Energy-Efficient Integrated Circuits by Touhid Bhuiyan
Cover of the book Management of Sexual Dysfunction in Men and Women by Touhid Bhuiyan
Cover of the book Applied Ontology Engineering in Cloud Services, Networks and Management Systems by Touhid Bhuiyan
Cover of the book Advanced Symbolic Analysis for VLSI Systems by Touhid Bhuiyan
Cover of the book Plasma Astrophysics, Part I by Touhid Bhuiyan
Cover of the book Quintessential Cities, Accountable to the Future by Touhid Bhuiyan
Cover of the book Female Pelvic Surgery by Touhid Bhuiyan
Cover of the book Death Threats and Violence by Touhid Bhuiyan
Cover of the book Master Techniques in Blepharoplasty and Periorbital Rejuvenation by Touhid Bhuiyan
Cover of the book 1,001 Celestial Wonders to See Before You Die by Touhid Bhuiyan
Cover of the book Lead-Free Piezoelectrics by Touhid Bhuiyan
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy